With an increasing number of cloud service providers (CSPs), research works on multi-cloud environments to provide solutions to avoid vendor lock-in and deal with the single-point failure problem have expanded considerably. However, a few schemes focus on the conditional privacy protection authentication of vehicular networks under a multi-cloud environment. In this regard, we propose a robust and extensible authentication scheme for vehicular networks to fulfil the evergrowing diversified service demands from users. According to our solution, the vehicles need to register with the trusted authority (TA) only once to achieve a fast and efficient authentication with CSPs. Additionally, as long as the new CSP is successfully registered in TA, it can participate in vehicular service. A cloud broker, which is managed by the TA, is responsible for connecting all the cloud services; consequently, the complexity involved in the selection of CSPs is hidden from the users' view. A detailed security analysis establishes that our scheme can fulfil conditional privacy protection and achieve the security objectives of vehicular networks. Our scheme is based on elliptic curve cryptography and does not employ the complex bilinear pairing operation. An evaluation of performance of the proposed scheme indicates that it is suitable for applications involving vehicular networks.
Network meta-analysis expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, synthesizing both direct and indirect information and thus strengthening inference. Since most of trials only compare two treatments, a typical data set in a network meta-analysis managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with significant missing data. Zhang et al. proposed an arm-based method accounting for correlations among different treatments within the same trial and assuming that absent arms are missing at random. However, in randomized controlled trials, nonignorable missingness or missingness not at random may occur due to deliberate choices of treatments at the design stage. In addition, those undertaking a network metaanalysis may selectively choose treatments to include in the analysis, which may also lead to missingness not at random. In this paper, we extend our previous work to incorporate missingness not at random using selection models. The proposed method is then applied to two network meta-analyses and evaluated through extensive simulation studies. We also provide comprehensive comparisons of a commonly used contrast-based method and the arm-based method via simulations in a technical appendix under missing completely at random and missing at random.
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